This award funds research that will examine whether incorporating machine learning algorithms into macroeconomic models can result in better ways to understand modern economies. The project will begin by assuming that individual consumers, workers, and firms can be modeled as making their decisions according to a particular type of machine learning algorithm (a boosting algorithm). The project seeks to understand the conditions under which a machine learning algorithm can emulate the decision-making process of a rational individual. The team will also analyze likely long run economic outcomes when these algorithms are used under various institutional and informational assumptions. The project, therefore, may develop a valuable new technique for modeling individual decisions in the context of an entire economic system. It could also help us understand how future economic outcomes may be affected by the increased use of machine learning methods to aid or even substitute for human decision making. The project could therefore help guide efforts to improve the competitiveness of the US economy.
The research team will exploit one of the central components of the machine learning algorithm, called the boosting algorithm, to build a highly accurate forecasting rule from a collection of rudimentary and possibly inaccurate forecasting rules. The usual approach in economic models is to assume that the agents (individuals, firms, etc.) are typically endowed with misspecified models. When this is the case, an individual or firm's decision-making process typically relies on simple, yet well fit, forecasting rules, which can differ from the true data generating process. The team aim to understand whether an agent endowed with flawed but well fit models can behave as if she knows the true data generating process. The team plans to pursue this objective in two steps. In the first part of the project, the team will examine learning dynamics under misspecified models. As an example, it examines a new class of learning models in which the agent has to learn the growth rate instead of the level of a variable of interest. In many macroeconomic models, the growth rate (e.g., inflation rate) rather than the level of a variable (e.g., price) is the main focus of the investigation. Assuming that the agent learns the growth rate through a recursive learning process rather than rational expectations may result in better explanations of important macroeconomic dynamics, such as recurrent hyperinflation and stock price volatility. The second step in the research plan investigates the dynamics of a specific machine learning algorithm with two research objectives: [1] if an agent is endowed with misspecified models, how the decision maker can test and build a new model to improve the forecast, and [2] what are the asymptotic properties of the processes of constructing new models, in particular whether the agent can emulate the rational agent in the long run.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
